Senior Data Engineer

What you'll do

• Build and maintain high-performance streaming and batch data pipelines for AI applications.
• Implement embedding generation workflows and vector store integrations for RAG systems and AI assistants.
• Develop scalable storage and retrieval patterns focused on cost-efficient architecture and production performance.
• Integrate pipelines with shared AI platform services including agent frameworks, registries, and feature stores.
• Embed end-to-end observability into data systems using metrics, structured logging, and automated alerts.
• Implement data quality validation, schema evolution safeguards, and governance compliance controls.
• Own the full development lifecycle from prototyping and implementation to testing and deployment.
• Influence design direction through technical proposals and hands-on delivery of production-ready AI capabilities.

What we're looking for

• Bachelor's degree or above in computer science or a related quantitative field, or equivalent practical experience.
• 5+ years of data engineering experience, including at least 1 year in a lead or senior technical role.
• Expert level skill in either Python or Java, with strong proficiency in the other.
• Proven experience building and scaling streaming data pipelines using tools like Kafka, Flink, Spark, or Kinesis.
• Experience with embedding pipelines and vector stores such as Pinecone, Weaviate, FAISS, or pgvector.
• Hands-on experience with workflow orchestration tools like Airflow or Dagster.
• Strong knowledge of data modeling, storage optimization, and retrieval patterns for large-scale systems.
• Experience integrating AI-ready data stores with LLM orchestration frameworks like LangChain or LangGraph (preferred).